On the mathematical foundations of learning
暂无分享,去创建一个
[1] I. J. Schoenberg. Metric spaces and completely monotone functions , 1938 .
[2] N. Aronszajn. Theory of Reproducing Kernels. , 1950 .
[3] A. Kolmogorov,et al. Entropy and "-capacity of sets in func-tional spaces , 1961 .
[4] J. Lamperti. ON CONVERGENCE OF STOCHASTIC PROCESSES , 1962 .
[5] V. Hutson. Integral Equations , 1967, Nature.
[6] H. Osborn. The Morse index theorem , 1967 .
[7] M. Birman,et al. PIECEWISE-POLYNOMIAL APPROXIMATIONS OF FUNCTIONS OF THE CLASSES $ W_{p}^{\alpha}$ , 1967 .
[8] R. A. Silverman,et al. Introductory Real Analysis , 1972 .
[9] Jean Duchon,et al. Splines minimizing rotation-invariant semi-norms in Sobolev spaces , 1976, Constructive Theory of Functions of Several Variables.
[10] S. Lang. Complex Analysis , 1977 .
[11] Peter Craven,et al. Smoothing noisy data with spline functions , 1978 .
[12] J. Meinguet. Multivariate interpolation at arbitrary points made simple , 1979 .
[13] Leslie G. Valiant,et al. A theory of the learnable , 1984, STOC '84.
[14] A. Pinkus. n-Widths in Approximation Theory , 1985 .
[15] S. Yau,et al. On the parabolic kernel of the Schrödinger operator , 1986 .
[16] A. Pietsch. Eigenvalues and S-Numbers , 1987 .
[17] W S McCulloch,et al. A logical calculus of the ideas immanent in nervous activity , 1990, The Philosophy of Artificial Intelligence.
[18] W. Pitts,et al. A Logical Calculus of the Ideas Immanent in Nervous Activity (1943) , 2021, Ideas That Created the Future.
[19] J. Herod. Introduction to Hilbert spaces with applications , 1990 .
[20] B. Carl,et al. Entropy, Compactness and the Approximation of Operators , 1990 .
[21] G. Wahba. Spline models for observational data , 1990 .
[22] Andrew R. Barron,et al. Complexity Regularization with Application to Artificial Neural Networks , 1991 .
[23] David Haussler,et al. Decision Theoretic Generalizations of the PAC Model for Neural Net and Other Learning Applications , 1992, Inf. Comput..
[24] George G. Lorentz,et al. Constructive Approximation , 1993, Grundlehren der mathematischen Wissenschaften.
[25] Heekuck Oh,et al. Neural Networks for Pattern Recognition , 1993, Adv. Comput..
[26] M. Shubin. Partial Differential Equations VII : Spectral Theory of Differential Operators , 1994 .
[27] A. Magnus. Constructive Approximation, Grundlehren der mathematischen Wissenschaften, Vol. 303, R. A. DeVore and G. G. Lorentz, Springer-Verlag, 1993, x + 449 pp. , 1994 .
[28] J. Navarro-Pedreño. Numerical Methods for Least Squares Problems , 1996 .
[29] Peter L. Bartlett,et al. The importance of convexity in learning with squared loss , 1998, COLT '96.
[30] Michael Taylor,et al. Partial Differential Equations I: Basic Theory , 1996 .
[31] Michael E. Taylor,et al. Partial Differential Equations , 1996 .
[32] Åke Björck,et al. Numerical methods for least square problems , 1996 .
[33] G. Lorentz,et al. Constructive approximation : advanced problems , 1996 .
[34] Thomas G. Dietterich. What is machine learning? , 2020, Archives of Disease in Childhood.
[35] H. Triebel,et al. Function Spaces, Entropy Numbers, Differential Operators: References , 1996 .
[36] C. Darken,et al. Constructive Approximation Rates of Convex Approximation in Non-hilbert Spaces , 2022 .
[37] S. Smale. Mathematical problems for the next century , 1998 .
[38] Vladimir Vapnik,et al. Statistical learning theory , 1998 .
[39] Partha Niyogi,et al. The Informational Complexity of Learning , 1998, Springer US.
[40] Lenore Blum,et al. Complexity and Real Computation , 1997, Springer New York.
[41] Tomaso A. Poggio,et al. Machine Learning, Machine Vision, and the Brain , 1999, AI Mag..
[42] Michael Shub,et al. Newton's method for overdetermined systems of equations , 2000, Math. Comput..
[43] S. Geer. Empirical Processes in M-Estimation , 2000 .
[44] Tomaso A. Poggio,et al. Regularization Networks and Support Vector Machines , 2000, Adv. Comput. Math..
[45] S. R. Jammalamadaka,et al. Empirical Processes in M-Estimation , 2001 .
[46] Bernhard Schölkopf,et al. Generalization Performance of Regularization Networks and Support Vector Machines via Entropy Numbers of Compact Operators , 1998 .
[47] S. Smale,et al. ESTIMATING THE APPROXIMATION ERROR IN LEARNING THEORY , 2003 .
[48] Aaas News,et al. Book Reviews , 1893, Buffalo Medical and Surgical Journal.